machine learning nptel assignment answers 2021

NPTEL Introduction to Machine Learning Assignment 1 Answers 2023

In this post, We have provided answers of NPTEL Introduction to Machine Learning Assignment 1. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

NPTEL Introduction To Machine Learning Week 1 Assignment Answer 2023

1. Which of the following is a supervised learning problem ?

• Grouping related documents from an unannotated corpus.
• Predicting credit approval based on historical data.
• Predicting if a new image has cat or dog based on the historical data of other images of cats and dogs, where you are supplied the information about which image is cat or dog.
• Fingerprint recognition of a particular person used in biometric attendance from the fingerprint data of various other people and that particular person.

2. Which of the following are classification problems?

• Predict the runs a cricketer will score in a particular match.
• Predict which team will win a tournament.
• Predict whether it will rain today.
• Predict your mood tomorrow.

3. Which of the following is a regression task?

• Predicting the monthly sales of a cloth store in rupees.
• Predicting if a user would like to listen to a newly released song or not based on historical data.
• Predicting the confirmation probability (in fraction) of your train ticket whose current status is waiting list based on historical data.
• Predicting if a patient has diabetes or not based on historical medical records.
• Predicting if a customer is satisfied or unsatisfied from the product purchased from ecommerce website using the the reviews he/she wrote for the purchased product.

4. Which of the following is an unsupervised learning task?

• Group audio files based on language of the speakers.
• Group applicants to a university based on their nationality.
• Predict a student’s performance in the final exams.
• Predict the trajectory of a meteorite.

5. Which of the following is a categorical feature?

• Number of rooms in a hostel.
• Gender of a person
• Your weekly expenditure in rupees.
• Ethnicity of a p e rson
• Area (in sq. centimeter) of your laptop screen.
• The color of the curtains in your room.
• Number of legs an animal.
• Minimum RAM requirement (in GB) of a system to play a game like FIFA, DOTA.

6. Which of the following is a reinforcement learning task?

• Learning to drive a cycle
• Learning to predict stock prices
• Learning to play chess
• Leaning to predict spam labels for e-mails

7. Let X and Y be a uniformly distributed random variable over the interval [0,4][0,4] and [0,6][0,6] respectively. If X and Y are independent events, then compute the probability, P(max(X,Y)>3)

• None of the above

9. Which of the following statements are true? Check all that apply.

• A model with more parameters is more prone to overfitting and typically has higher variance.
• If a learning algorithm is suffering from high bias, only adding more training examples may not improve the test error significantly.
• When debugging learning algorithms, it is useful to plot a learning curve to understand if there is a high bias or high variance problem.
• If a neural network has much lower training error than test error, then adding more layers will help bring the test error down because we can fit the test set better.

10. Bias and variance are given by :

• E[f^(x)]−f(x),E[(E[f^(x)]−f^(x)) 2 ]
• E[f^(x)]−f(x),E[(E[f^(x)]−f^(x))] 2
• (E[f^(x)]−f(x))2,E[(E[f^(x)]−f^(x)) 2 ]
• (E[f^(x)]−f(x))2,E[(E[f^(x)]−f^(x))] 2

NPTEL Introduction to Machine Learning Assignment 1 Answers 2022 [July-Dec]

1. Which of the following are supervised learning problems? (multiple may be correct) a. Learning to drive using a reward signal. b. Predicting disease from blood sample. c. Grouping students in the same class based on similar features. d. Face recognition to unlock your phone.

2. Which of the following are classification problems? (multiple may be correct) a. Predict the runs a cricketer will score in a particular match. b. Predict which team will win a tournament. c. Predict whether it will rain today. d. Predict your mood tomorrow.

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3. Which of the following is a regression task? (multiple options may be correct) a. Predict the price of a house 10 years after it is constructed. b. Predict if a house will be standing 50 years after it is constructed. c. Predict the weight of food wasted in a restaurant during next month. d. Predict the sales of a new Apple product.

4. Which of the following is an unsupervised learning task? (multiple options may be correct) a. Group audio files based on language of the speakers. b. Group applicants to a university based on their nationality. c. Predict a student’s performance in the final exams. d. Predict the trajectory of a meteorite.

5. Given below is your dataset. You are using KNN regression with K=3. What is the prediction for a new input value (3, 2)?

6. Which of the following is a reinforcement learning task? (multiple options may be correct)

7. Find the mean of squared error for the given predictions:

8. Find the mean of 0-1 loss for the given predictions:

👇 For Week 02 Assignment Answers 👇

9. Bias and variance are given by:

10. Which of the following are true about bias and variance? (multiple options may be correct)

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About Introduction to Machine Learning

With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms. 

COURSE LAYOUT

• Week 0:  Probability Theory, Linear Algebra, Convex Optimization – (Recap)
• Week 1:  Introduction: Statistical Decision Theory – Regression, Classification, Bias Variance
• Week 2:  Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares
• Week 3:  Linear Classification, Logistic Regression, Linear Discriminant Analysis
• Week 4:  Perceptron, Support Vector Machines
• Week 5:  Neural Networks – Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation – MLE, MAP, Bayesian Estimation
• Week 6:  Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees – Instability Evaluation Measures
• Week 7:  Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods – Bagging, Committee Machines and Stacking, Boosting
• Week 8:  Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian Networks
• Week 9:  Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation
• Week 10:  Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering
• Week 11:  Gaussian Mixture Models, Expectation Maximization
• Week 12:  Learning Theory, Introduction to Reinforcement Learning, Optional videos (RL framework, TD learning, Solution Methods, Applications)

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course. Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

NPTEL Introduction to Machine Learning Assignment 1 Answers [Jan – June 2022]

Q1. Which of the following is a supervised learning problem? 

a. Grouping related documents from an unannotated corpus.  b. Predicting credit approval based on historical data  c. Predicting rainfall based on historical data  d. Predicting if a customer is going to return or keep a particular product he/she purchased from e-commerce website based on the historical data about the customer purchases and the particular product.  e. Fingerprint recognition of a particular person used in biometric attendance from the fingerprint data of various other people and that particular person

Answer:- b, c, d , e

Q2. Which of the following is not a classification problem? 

a. Predicting the temperature (in Celsius) of a room from other environmental features (such as atmospheric pressure, humidity etc).  b.Predicting if a cricket player is a batsman or bowler given his playing records.  c. Predicting the price of house (in INR) based on the data consisting prices of other house (in INR) and its features such as area, number of rooms, location etc.  d. Filtering of spam messages  e. Predicting the weather for tomorrow as “hot”, “cold”, or “rainy” based on the historical data wind speed, humidity, temperature, and precipitation.

Answer:- a, c

Q3. Which of the following is a regression task? (multiple options may be correct) 

a. Predicting the monthly sales of a cloth store in rupees.  b. Predicting if a user would like to listen to a newly released song or not based on historical data.  c. Predicting the confirmation probability (in fraction) of your train ticket whose current status is waiting list based on historical data.  d. Predicting if a patient has diabetes or not based on historical medical records.  e. Predicting if a customer is satisfied or unsatisfied from the product purchased from e-commerce website using the the reviews he/she wrote for the purchased product.

Q4. Which of the following is an unsupervised task? 

a. Predicting if a new edible item is sweet or spicy based on the information of the ingredients, their quantities, and labels (sweet or spicy) for many other similar dishes.  b. Grouping related documents from an unannotated corpus.  c. Grouping of hand-written digits from their image.  d. Predicting the time (in days) a PhD student will take to complete his/her thesis to earn a degree based on the historical data such as qualifications, department, institute, research area, and time taken by other scholars to earn the degree.  e. all of the above

Answer:- c, d

Q5. Which of the following is a categorical feature? 

a. Number of rooms in a hostel.  b. Minimum RAM requirement (in GB) of a system to play a game like FIFA, DOTA.  c. Your weekly expenditure in rupees.  d. Ethnicity of a person  e. Area (in sq. centimeter) of your laptop screen.  f. The color of the curtains in your room.

Answer:- d, f

Q6. Let X and Y be a uniformly distributed random variable over the interval [0, 4] and [0, 6] respectively. If X and Y are independent events, then compute the probability, P(max(X,Y)>3

a. 1/6 b. 5/6 c. 2/3 d. 1/2 e. 2/6 f. 5/8 g. None of the above

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Q7. Let the trace and determinant of a matrix A[acbd] be 6 and 16 respectively. The eigenvalues of A are

Q8. What happens when your model complexity increases? (multiple options may be correct) 

a. Model Bias decreases  b. Model Bias increases  c. Variance of the model decreases  d. Variance of the model increases

Answer:- a, d

Q9. A new phone, E-Corp X1 has been announced and it is what you’ve been waiting for, all along. You decide to read the reviews before buying it. From past experiences, you’ve figured out that good reviews mean that the product is good 90% of the time and bad reviews mean that it is bad 70% of the time. Upon glancing through the reviews section, you find out that the X1 has been reviewed 1269 times and only 172 of them were bad reviews. What is the probability that, if you order the X1, it is a bad phone? 

a. 0.136  b. 0.160  c. 0.360  d. 0.840  e. 0.773  f. 0.573  g. 0.181

Q10. Which of the following are false about bias and variance of overfitted and underfitted models? (multiple options may be correct) 

a. Underfitted models have high bias.  b. Underfitted models have low bias.  c. Overfitted models have low variance.  d. Overfitted models have high variance.

NPTEL Introduction to Machine Learning Assignment 1 Answers 2022:- In This article, we have provided the answers of Introduction to Machine Learning Assignment 1.

Disclaimer :- We do not claim 100% surety of solutions, these solutions are based on our sole expertise, and by using posting these answers we are simply looking to help students as a reference, so we urge do your assignment on your own.

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NPTEL Introduction to Machine Learning – IITM Assignment 2021

• by QuizXp Team
• July 28, 2021 October 20, 2021

NPTEL INTRODUCTION TO MACHINE LEARNING – IITM course aimed at helping students enable data-driven disciplines with the increased availability of a variety of data from varied sources There has been increasing attention paid to the various methods of analytics and machine learning.

NPTEL INTRODUCTION TO MACHINE LEARNING is a MOOC course offered by IIT Madras on the NPTEL platform. This course is intend to introduce some of the basic concepts of machine learning The course is developed by Prof. Balaraman Ravindran is currently a Professor in Computer Science at IIT Madras and Mindtree Faculty Fellow.

• Who Can Join: This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD
• Requirements/Prerequisites:  We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well.
• INDUSTRY SUPPORT:  Any company in the data analytics/data science/big data domain would value this course.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the average of the best 8 assignments out of the total 12 assignments given in the course. Exam score = 75% of the proctored certification exam score out of 100 Final score = Average assignment score + Exam score

Students will be eligible for CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If any of the 2 criteria are not met, the student will not get the certificate even if the Final score >= 40/100.

NPTEL Introduction to machine learning Assignment Week 12 Answers:-

Q1. In solving a classification problem, if in the learned model there is a large difference between the output of the learned model and the expected output of the learned model over various sources of variability, then we can expect _ the component of the generalisation error to be high.

Q2. Given below are some properties of different classification algorithms. In which among the following would you expect feature

Answer:- A,B,D

Q3. Which of the following measure best analyze the performance of a classifier?

Q4. As discussed in the lecture, most of the classifiers minimize the empirical risk. Which among the following is an exceptional case?

Q5. What do you expect to happen to the variance component of the generalisation error of your model as the size of the training data set increases?

Q6. What happens when your model complexity (such as interaction terms in linear regression, order of polynomial in SVM, etc.) increases?

Answer:- b,c

Q7. Suppose we want an RL agent to learn to play the game of golf. For training purposes, we make use of a golf simulator program. Assume

Q8. You want to toss a fair coin a number of times and obtain the probability of getting heads by taking a simple average. What is the

Q9. You face a particularly challenging RL problem, where the reward distribution keeps changing with time. In order to gain maximum

NPTEL Introduction to machine learning Assignment Week 11 Answers:-

Q1. During parameter estimation for a GMM model using data X, which of the following quantities are you minimizing (directly or indirectly)?

Q2. When executing the Expectation Maximization algorithm, a common problem is the sheer complexity of the number of parameters to estimate. For a typical K-Gaussian Mixture Model in an n-dimensional space, how many independent parameters are being estimated in total?

Q3. Which of the following is an assumption that reduces Gaussian Mixture Models to K-means?

Q4. Given N samples x 1, x 2,…, xN drawn independently from a Gaussian distribution with variance σ 2 and unknown mean μ . Assume that the prior distribution of the mean is also a Gaussian distribution, but with parameters mean μp and variance σ 2 p . Find the MAP estimate of the mean.

Q5. You are presented with a dataset that has hidden/missing variables that influences your data. You are asked to use Expectation Maximization algorithm to best capture the data. How would you define the E and M in Expectation Maximization?

Q6. During parameter estimation for a GMM model using data X, which of the following quantities are you minimizing (directly or indirectly)?

Q7. You are given n p-dimensional data points. The task is to learn a classifier to distinguish between k classes. You come to know that the dataset has missing values. Can you use EM algorithm to fill in the missing values ? (without making any further assumptions)

NPTEL Introduction to machine learning Assignment Week 10 Answers:-

Q1. Considering single-link and complete-link hierarchical clustering, is it possible for a point to be closer to points in other clusters than to points in its own cluster? If so, in which approach will this tend to be observed?

Q2. Consider the following one dimensional data set: 12, 22, 2, 3, 33, 27, 5, 16, 6, 31, 20, 37, 8 and 18. Given k = 3 and initial cluster centers to be 5, 6 and 31, what are the final cluster centres obtained on applying the k -means algorithm?

All the best for the final exam, for extra preparation, take our membership for better score in exam read more here:- Final Exam Membership

Q3. For the previous question, in how many iterations will the k-means algorithm converge?

Q4. In the lecture on the BIRCH algorithm, it is stated that using the number of points N , sum of points SUM and sum of squared points SS , we can determine the centroid and radius of the combination of any two clusters A and B. How do you determine the centroid of the combined cluster? (In terms of N,SUM and SS of both the clusters)

Q5. What assumption does the CURE clustering algorithm make with regards to the shape of the clusters?

Q6. What would be the effect of increasing MinPts in DBSCAN while retaining the same Eps parameter? (Note that more than one statement may be correct)

Q7. Visualize the dataset DS1. Which of the following algorithms will be able to recover the true clusters (first check by visual inspection and then write code to see if the result matches to what you expected).

Q8. For two independent runs of K-Mean clustering is it guaranteed to get same clustering results? Note: seed value is not preserved in independent runs.

Q9. Consider the similarity matrix given below: Which of the following shows the hierarchy of clusters created by the single link clustering algorithm.

Q10. For the similarity matrix given in the previous question, which of the following shows the hierarchy of clusters created by the complete link clustering algorithm.

NPTEL Introduction to machine learning Assignment Week 9 Answers:-

Q1. Consider the bayesian network shown below.

Two students – Manish and Trisha make the following claims:

• Manish claims P(D|{S, L, C}) = P(D|{L, C}) • Trisha claims P(D|{S, L}) = P(D|L)

Q2. Consider the Bayesian graph shown below in Figure 2.

Q3. Using the data given in the previous question, compute the probability of following assignment, P ( i =1, g =1, s =1, l =0) irrespective of the difficulty of the course? (up to 3 decimal places)

Q4. Consider the Bayesian network shown below in Figure 3

• Trisha claims P(H|{S, G, J}) = P(H|{G, J}) • Manish claims P(H|{S, C, J}) = P(H|{C, J})

Q5. Consider the Markov network shown below in Figure 4

Which of the following variables are NOT in the markov blanket of variable “4” shown in the above Figure 4 ? (multiple answers may be correct)

Answer:- d,g

Q6. In the Markov network given in Figure 4, two students make the following claims:

• Manish claims variable “1” is dependent on variable “7” given variable “2”. • Trina claims variable “2” is independent of variable “6” given variable “3”.

Q7. Four random variables are known to follow the given factorization

P ( A 1= a 1, A 2= a 2, A 3= a 3, A 4= a 4)=1 Z ψ 1( a 1, a 2) ψ 2( a 1, a 4) ψ 3( a 1, a 3) ψ 4( a 2, a 4) ψ 5( a 3, a 4)

The corresponding Markov network would be

Q8. Consider the following Markov Random Field.

Which of the following nodes will have no effect on H given the Markov Blanket of H?

Answer:- c,e,f

Q9. Select the correct pairs of (Inference Algorithm, Graphical Model) (note: more than one option may be correct)

Answer:- will update this answers soon and notify on telegram

Q10. Here is a popular toy graphical model. It models the grades obtained by a student in a course and it’s implications. Difficulty represents the difficulty of the course and intelligence is an indicator of how intelligent the student is, SAT represents the SAT scores of the student and Letter presents the event of the student receiving a letter of recommendation from the faculty teaching the course.

Answer:- a,c,d

NPTEL Introduction to machine learning Assignment Week 8 Answers:-

Q1. In a given classification problem, there are 6 different classes. In building a classification model, we want to penalise specific errors made by the model depending upon the actual and predicted class label. For example, given a training data point belonging to class 1, if the model predicts it as class 2, then the penalty for this will be different if for the same data point, the model had predicted it as class 3. To build such a model, we need to select an appropriate

Q2. The Naive Bayes classifier makes the assumption that the ________ are independent given the ________ .

Q3. Consider the problem of learning a function X → Y , where Y is Boolean. X is an input vector ( X 1, X 2), where X 1 is categorical and takes 3 values, and X 2 is a continuous variable (normally distributed). What would be the minimum number of parameters required to define a Naive Bayes model for this function?

Q4. In boosting, the weights of data points that were miscalssified are _________ as training progresses.

Q5. In a random forest model let m << p be the number of randomly selected features that are used to identify the best split at any node of a tree. Which of the following are true? ( p is the original number of features) (Multiple options may be correct)

Q6. Consider the following data for 500 instances of home, 600 instances of office and 700 instances of factory type buildings

Q7. Consider the following graphical model, which of the following are false about the model? (multiple options may be correct)

Answer:- a,b

Q8. Consider the Bayesian network given in the previous question. Let ‘A’, ‘B’, ‘C’, ‘D’and ‘E’denote the random variables shown in the network. Which of the following can be inferred from the network structure?

NPTEL Introduction to machine learning Assignment Week 7 Answers:-

Q1. For the given confusion matrix, compute the recall

Q2. Which of the following are true? TP – True Positive, TN – True Negative, FP – False Positive, FN – False Negative

Answer:- a,c

Q3. How does bagging help in improving the classification performance?

Q4. Which method among bagging and stacking should be chosen in case of limited training data? and what is the appropriate reason for your preference?

Q5. Which of the following statements are false when comparing Committee Machines and Stacking

Q6. Which of the following measure best analyze the performance of a classifier?

Q7. For the ROC curve of True positive rate vs False positive rate, which of the following are true?

Q8. Which of the following are true about using 5-fold cross validation with a data set of size n = 100 to select the value of k in the kNN algorithm.

NPTEL Introduction to machine learning Assignment Week 6 Answers:-

Q1. Decision trees can be used for __________ .

Q2. In building a decision tree model, to control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true?

Q3. In a decision tree, if we decide to swap out the usual splits (of the form xi < k or xi > k ) and instead used a linear combination of features instead, (like βTX + β 0 ), where the parameters of the hyperplane β , β 0 are also simultaneously learnt, which of the following statements would be true?

Answer:- b,d

Q4. Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. The average value of the outputs of data points denotes the response of a branch. The original responses for data points along the two branches (left right respectively) were response _ left and, response _ right and the new response after collapsing the node is response _ new . What are the values for response _ left , response _ right and response _ new (numbers in the option are given in the same order)?

Q5. Which among the following split-points for the feature 1 would give the best split according to the information gain measure?

Q6. For the same dataset, which among the following split-points for feature2 would give the best split according to the gini index measure?

Q7. In which of the following situations is it appropriate to introduce a new category ’Missing’ for missing values? (multiple options may be correct)

Answer:- a,d

NPTEL Introduction to machine learning Assignment Week 5 Answers:-

Q4. Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch,

Q1. You are given the N samples of input (x) and output (y) as shown in the figure below. What will be the most appropriate model y = f ( x )

Q2. Given N samples x 1, x 2,…, xN drawn independently from a Gaussian distribution with variance σ 2 and unknown mean μ , find the MLE of the mean.

Q3. Consider the following function.

Q4. Using the notations used in class, evaluate the value of the neural network with a 3-3-1 architecture (2-dimensional input with 1 node for the bias term in both the layers). The parameters are as follows

Answer:- WILL BE UPDATED BY MIDNIGHT AND WILL NOTIFY ON TELEGRAM , CLICK ON BELOW IMAGE FOR LINK

Q5. Which of the following statements are true:

Answer:- B,C

Q6. We have a function which takes a two-dimensional input x =( x 1, x 2) and has two parameters w =( w 1, w 2) given by f ( x , w )= σ ( σ ( x 1 w 1) w 2+ x 2) where σ ( x )=11+ e − x .We use backpropagation to estimate the right parameter values. We start by setting both the parameters to 2. Assume that we are given a training point x 2=1, x 1=0, y =3. Given this information answer the next two questions. What is the value of ∂ f ∂ w 2.

Q7. If the learning rate is 0.5, what will be the value of w 2 after one update using backpropagation algorithm?

Q8. Which of the following are true when comparing ANNs and SVMs?

Q9. Which of the following are correct?

Q10. Which of the following are false?

NPTEL Introduction to machine learning Assignment Week 4 Answers:-

Q1. Suppose we use a linear kernel SVM to build a classifier for a 2-class problem where the training data points are linearly separable. In general, will the classifier trained in this manner produce the same decision boundary as the classifier trained using the perceptron training algorithm on the same training data?

Q2. Consider the data set given below. Claim: PLA (perceptron learning algorithm) can be used to learn a classifier that achieves zero misclassification error on the training data. This claim is:

Q3. For a support vector machine model, let xi be an input instance with label yi . If yi ( β ^0+ xTiβ ^)>1 where β 0 and β ^ are the estimated parameters of the model, then

Q4. Suppose we use a linear kernel SVM to build a classifier for a 2-class problem where the training data points are linearly separable. In general, will the classifier trained in this manner be always the same as the classifier trained using the perceptron training algorithm on the same training data?

Q5. Train a linear regression model (without regularization) on the above dataset.Report the coefficients of the best fit model. Report the coefficients in the following format: β 0 β 1 β 2 β 3.

Q6. Train an l2 regularized linear regression model on the above dataset. Vary the regularization parameter from 1 to 10. As you increase the regularization parameter, absolute value of the coefficients (excluding the intercept) of the model:

Q7. Train an l 2 regularized logistic regression classifier on the modified iris dataset. We recommend using sklearn. Use only the first two features for your model. We encourage you to explore the impact of varying different hyperparameters of the model. Kindly note that the C parameter mentioned below is the inverse of the regularization parameter λ . As part of the assignment train a model with the following hyperparameters: Model: logistic regression with one-vs-rest classifier, C =1 e 4 For the above set of hyperparameters, report the best classification accuracy

Q8. Train an SVM classifier on the modified iris dataset. We recommend using sklearn. Use only the first two features for your model. We encourage you to explore the impact of varying different hyperparameters of the model. Specifically try different kernels and the associated hyperparameters. As part of the assignment train models with the following set of hyperparameters RBF-kernel, gamma = 0.5, one-vs-rest classifier, no-feature-normalization. Try C = 0.01, 1, 10. For the above set of hyperparameters, report the best classification accuracy along with total number of support vectors on the test data.

NPTEL Introduction to machine learning Assignment Week 3 Answers:-

Q1. Consider the case where two classes follow Gaussian distribution which are centered at (4, 7) and (−4, −1) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

Q2. Consider the following data with two classes. The color indicates different class.

Q3. We discussed the use of MLE for the estimation of parameters of logistic regression model. We used which of the following assumptions to derive the likelihood function ?

Q4. Which of the following statements is true about LDA regarding outliers?

Q5. Consider the following distribution of training data:

Q6. Suppose that we have two variables, X and Y (the dependent variable). We wish to find the relation between them. An expert tells us that relation between the two has the form Y = m log( X )+ c . Available to us are samples of the variables X and Y . Is it possible to apply linear regression to this data to estimate the values of m and c ?

Q7. In a binary classification scenario where x is the independent variable and y is the dependent variable, logistic regression assumes that the conditional distribution y | x follows a

Q8. Assuming that you apply LDA to this data, what is the estimated covariance matrix?

Answer:- F (THIS MIGHT BE WRONG PLEASE CHECK AT YOUR LEVEL)

Q9. Given the following 3D input data, identify the principal component. (Steps: center the data, calculate the sample covariance matrix, calculate the eigenvectors and eigenvalues, identify the principal component)

Answer:- B (THIS MIGHT BE WRONG PLEASE CHECK AT YOUR LEVEL)

Q10. For the data given in the previous question, find the transformed input along the first two principal components.

NPTEL Introduction to machine learning Assignment Week 2 Answers:-

Q1. Given a training dataset, the following visualization shows the fit of three different models (in blue line). Assume that the test data and training data come from the same distribution. What can you conclude from the following visualizations? Multiple options can be correct.

Answer:- A,C,D

Q2. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option below which describes relationship of bias and variance with lambda.

Q3. Given a training data set of 10,000 instances, with each input instance having 17 dimensions and each output instance having 2 dimensions, the dimensions of the design matrix used in applying linear regression to this data is

Q4. Suppose we want to add a regularizer to the linear regression loss function, to control the magnitudes of the weights β . We have a choice between Ω1( β )=∑ i =1 p | β | and Ω2( β )=∑ i =1 pβ 2. Which one is more likely to result in sparse weights?

Q5. Consider forward selection, backward selection and best subset selection with respect to the same data set. Which of the following is true?

Q6. In the formulation of the method, we observe that in iteration k , we regress the entire dataset on z 0, z 1,… zk −1 . It seems like a waste of computation to recompute the coefficients for z 0 a total of p times, z 1 a total of p −1 times and so on. Can we re-use the coefficients computed in iteration j for iteration j +1 for zj −1 ?

Q7. Consider the following five training examples We want to learn a function f ( x ) of the form f ( x )= ax + b which is parameterised by ( a , b ). Using squared error as the loss function, which of the following parameters would you use to model this function to get a solution with the minimum loss.

Q8. Here is a data set of words in two languages.

NPTEL Introduction to machine learning Assignment Week 1 Answers:-

Q1 . Which of the following is a supervised learning problem?

Answer:- B,C,D

Q2 – Which of the following is not a classification problem?

Answer:- A,C

Q3 – Which of the following is a regression task? (multiple options may be correct)

Note:- WE NEVER PROMOTE COPYING AND We do not claim 100% surety of answers, these answers are based on our sole knowledge, and by posting these answers we are just trying to help students to reference, so we urge do you assignment on your own.

Also Check:- INTERNSHIP OPPORTUNITIES

Q4 – Which of the following is an unsupervised task?

Answer:- C,D

Q5 – Which of the following is a categorical feature?

Answer:- D,F

Q6 – Let X and Y be a uniformly distributed random variable over the interval [0, 4] and [0, 6] respectively. If X and Y are independent events, then compute the probability, P(max( X , Y )>3)

Answer:- F – 5/8

NOTE:- IF THERE IS ANY CHANGE IN ANSWERS OF NPTEL Introduction to Machine Learning WILL UPDATE BEFORE LAST DATE AND NOTIFY ON TELEGRAM OR WHATSAPP. SO KINDLY JOIN US, CLICK ON BELOW IMAGE AND JOIN US.

Q7 – Let the trace and determinant of a matrix A [ acbd ] be 6 and 16 respectively. The eigenvalues of A are.

Answer:-E -3+ ı 7–√,3− ı 7–√where ı =−1

Q8 – What happens when your model complexity increases? (multiple options may be correct)

Q9 – A new phone, E-Corp X1 has been announced and it is what you’ve been waiting for, all along. You decide to read the reviews before buying it. From past experiences, you’ve figured out that good reviews mean that the product is good 90% of the time and bad reviews mean that it is bad 70% of the time. Upon glancing through the reviews section, you find out that the X1 has been reviewed 1269 times and only 172 of them were bad reviews. What is the probability that, if you order the X1, it is a bad phone?

Answer:- G – 0.181

Q10 – Which of the following are false about bias and variance of overfitted and underfitted models? (multiple options may be correct)

NPTEL Introduction to machine learning Assignment Week 0 Answers:-

Q1. There are n bins of which the k -th bin contains k −1 blue balls and n − k red balls. You pick a bin at random and remove two balls at random without replacement. Find the probability that:

Answer:- C – 1/2,2/3

Q2. A medical company touts its new test for a certain genetic disorder. The false negative rate is small: if you have the disorder, the probability that the test returns a positive result is 0.999. The false positive rate is also small: if you do not have the disorder, the probability that the test returns a positive result is only 0.005. Assume that 2% of the population has the disorder. If a person chosen uniformly from the population is tested and the result comes back positive, what is the probability that the person has the disorder?

Answer:- A – 0.803

Q3. In an experiment, n coins are tossed, with each one showing up heads with probability p independently of the others. Each of the coins which shows up heads is then tossed again. What is the probability of observing 5 heads in the second round of tosses, if we toss 15 coins in the first round and p = 0.4?

Answer:- B – 0.055

Q4. Consider two random variables X and Y having joint density function f ( x , y )=2 e − x − y ,< x < y <∞. Are X and Y independent? Find the covariance of X and Y .

Answer:- A – Yes, 1/4

Q5. An airline knows that 5 percent of the people making reservations on a certain flight will not show up. Consequently, their policy is to sell 52 tickets for a flight that can hold only 50 passengers. What is the probability that there will be a seat available for every passenger who shows up?

Answer:- D – 0.7405

Q6. Let X have mass function  f ( x )={{ x ( x +1)}−10if x =1,2,…,otherwise,

Answer:- B – α <1

Q7. Is the following a distribution function?

Answer:- A – Yes, x −2 e −1/ x , x >0

Q8. Can the value of a probability density function be greater than one? What about the cumu- lative distribution function?

Answer:- B – PDF: yes, CDF: no

Q9. You are given a biased coin with probability of seeing a head is p = 0.6 and probability of seeing a tail is q = 0.4. Suppose you toss the coin 10 times, what is the probability of you getting the head at most 2 times? Also, what is the probability of you getting the head for the first time on your fourth attempt?

Answer:- A – 0.012, 0.038

Q10. Given a bag containing 6 red balls, 4 blue balls and 7 green balls, what is the probability that in 5 trials, at least 3 red balls are drawn from the bag?

Answer:- A – 0.24

Q11. In the experiment from the previous question, what is the probability of picking a red ball for the first time on the fourth attempt?

Answer:- C – 0.096

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NPTEL: Exam Registration is open now for Jan 2022 courses!

Dear Candidate,

Here is a golden opportunity for those who had previously enrolled in this course during the Jan 2021 semester, but could not participate in the exams or were absent/did not pass the exam for this course. This course is being reoffered in Jan 2022 and we are giving you another chance to write the exam in April 2022 and obtain a certificate based on NPTEL norms. Do not let go of this unique opportunity to earn a certificate from the IITs/IISc.

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Please fill the exam form using the same Enrolled email id & make fee payment via the form, as before.

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5. You DO NOT have to resubmit Assignments OR participate in the non-proctored 

programming exams.

6. If you do enroll to Jan 2022 course, we will take the best average assignment scores/non-proctored programming exam score across the two semesters

Our suggestion:

- Please check once if you have >= 40/100  in average assignment score and also participate in the non-proctored programming exams that will be conducted during this semester in the course to become eligible for the e-certificate, wherever applicable.

- If not, please submit Assignments again in the Jan 2022 course & and also participate in the non-proctored programming exams to become eligible for the e-certificate.

- You can also submit Assignments again and participate in the non-proctored programming exams if you want to better your previous scores.

RECOMMENDATION: Please enroll to the Jan 2022 course and brush up your lessons for the exam.

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If you register for the exam and pay before March 14, 2022, 10:00 AM, Exam fees will be Rs. 1000/- per exam . 

If you register for exam before March 14, 2022, 10:00 AM and have not paid or if you register between March 14, 2022, 10:00 AM & March 18, 2022, 10:00 AM, Exam fees will be Rs. 1500/- per exam 

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The hall ticket will be available for download tentatively by 2 weeks prior to the exam date . We will confirm the same through an announcement once it is published. 

12. FOR CANDIDATES WHO WOULD LIKE TO WRITE MORE THAN 1 COURSE EXAM:- you can add or delete courses and pay separately – till the date when the exam form closes. Same day of exam – you can write exams for 2 courses in the 2 sessions. Same exam center will be allocated for both the sessions. 

13. Data changes: 

Last date for data changes: March 18, 2022 10:00 AM :  

All the fields in the Exam form except for the following ones can be changed until the form closes. 

The following 6 fields can be changed ONLY when there are NO courses in the course cart. And you will be able to edit the following fields only if you: - 

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Note: Once you remove or cancel a course, you will be able to edit these fields immediately. 

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Domain Certification helps learners to gain expertise in a specific Area/Domain. This can be helpful for learners who wish to work in a particular area as part of their job or research or for those appearing for some competitive exam or becoming job ready or specialising in an area of study.  

Every domain will comprise Core courses and Elective courses. Once a learner completes the requisite courses per the mentioned criteria, you will receive a Domain Certificate showcasing your scores and the domain of expertise. Kindly refer to the following link for the list of courses available under each domain: https://nptel.ac.in/noc/Domain/discipline.html

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Thank you for learning with NPTEL!!

Dear Learner, Thank you for taking the course with NPTEL!! Hope you enjoyed the journey with us. The results for this course have been published and we are closing this course now.  You will still have access to the contents and assignments of this course, if you click on the course name from the "Mycourses" tab on swayam.gov.in. The discussion forum is being closed though and you cannot ask questions here. For any further queries please write to [email protected] . - Team NPTEL

Introduction to Machine Learning: Result Published!

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Feedback for Introduction to Machine Learning

Dear student, We are glad that you have attended the NPTEL online certification course. We hope you found the NPTEL Online course useful and have started using NPTEL extensively. In this regard, we would like to have feedback from you regarding our course and whether there are any improvements, you would like to suggest.   We are enclosing an online feedback form and would request you to spare some of your valuable time to input your observations. Your esteemed input will help us in serving you better. The link to give your feedback is: https://docs.google.com/forms/d/1c0IyKJNdR4pyBPYF9Scj7som_yjqOhHcVQulMJb_SSQ/viewform We thank you for your valuable time and feedback. Thanks & Regards, -NPTEL Team

Introduction to Machine Learning: Open now for exam registration July 2021!!

Dear Candidate, Here is a golden opportunity for those who had previously enrolled in this course during the  Jan 2021  semester, but could not participate in the exams or were absent/did not pass the exam for this course. This course is being reoffered in July 2021 and we are giving you another chance to write the exam in Sep/Oct 2021 and obtain a certificate based on NPTEL norms. Do not let go of this unique opportunity to earn a certificate from the IITs/IISc. IMPORTANT instructions for learners - Please read this carefully 1. The exam date for this course:  October 24, 2021 2. Certification exam registration URL is:  https://examform.nptel.ac.in/     Please fill the exam form using the  same Enrolled email id  & make fee payment via the form, as before. 3. Choose from the Cities where exam will be conducted:   Exam Cities 4. You DO NOT have to re-enroll in the courses.  5. You DO NOT have to resubmit Assignments OR participate in the non-proctored  programming exams. 6. If you do enroll to July 2021 course, we will take the best average assignment scores/non-proctored programming exam score across the two semesters Our suggestion: - Please check once if you have >= 40/100  in average assignment score and also participate in the non-proctored programming exams that will be conducted during this semester in the course to become eligible for the e-certificate, wherever applicable. - If not, please submit Assignments again in the July 2021 course & and also participate in the non-proctored programming exams to become eligible for the e-certificate. - You can also submit Assignments again and participate in the non-proctored programming exams if you want to better your previous scores. RECOMMENDATION:  Please enroll to the July 2021 course and brush up your lessons for the exam. 7.  Exam fees:  If you register for the exam and pay before  Sep 13, 2021, 10:00 AM , Exam fees will be  Rs. 1000/- per exam .  If you register for exam before  Sep 13, 2021, 10:00 AM  and have not paid or if you register between  Sep 13, 2021, 10:00 AM & Sep 17, 2021, 5:00 PM , Exam fees will be  Rs. 1500/-  per exam  8. 50% fee waiver for the following categories:  Students belonging to the SC/ST category: please select Yes for the SC/ST option and upload the correct Community certificate. Students belonging to the PwD category with more than 40% disability: please select Yes for the option and upload the relevant Disability certificate.  9. Last date for exam registration: Sep 17, 2021, 5:00 PM (Friday).   10. Mode of payment: Online payment - debit card/credit card/net banking.  11.  HALL TICKET:  The hall ticket will be available for download tentatively by  2 weeks prior to the exam date  . We will confirm the same through an announcement once it is published.  12. FOR CANDIDATES WHO WOULD LIKE TO WRITE MORE THAN 1 COURSE EXAM:- you can add or delete courses and pay separately – till the date when the exam form closes. Same day of exam – you can write exams for 2 courses in the 2 sessions. Same exam center will be allocated for both the sessions.  13.  Data changes:   Last date for data changes: Sep 17, 2021, 5:00 PM:  All the fields in the Exam form except for the following ones can be changed until the form closes.  The following 6 fields can be changed ONLY when there are NO courses in the course cart. And you will be able to edit the following fields only if you: -  REMOVE unpaid courses from the cart And/or - CANCEL paid courses  1. Do you come under the SC/ST category? *  2. SC/ST Proof  3. Are you a person with disabilities? *  4. Are you a person with disabilities above 40%?  5. Disabilities Proof  6. What is your role ?  Note:  Once you remove or cancel a course, you will be able to edit these fields immediately.  But, for cancelled courses, refund of fees will be initiated only after 2 weeks.  14.  LAST DATE FOR CANCELLING EXAMS and getting a refund: Sep 17, 2021, 5:00 PM   15. Click here to view Timeline and Guideline :  Guideline   Domain Certification Domain Certification helps learners to gain expertise in a specific Area/Domain. This can be helpful for learners who wish to work in a particular area as part of their job or research or for those appearing for some competitive exam or becoming job ready or specialising in an area of study.     Every domain will comprise Core courses and Elective courses. Once a learner completes the requisite courses per the mentioned criteria, you will receive a Domain Certificate showcasing your scores and the domain of expertise. Kindly refer to the following link for the list of courses available under each domain:  https://nptel.ac.in/noc/Domain/discipline.html Thanks & Regards,  NPTEL TEAM

April 2021 NPTEL Exams have been postponed!

Dear learner Taking the current covid situation into consideration, the NPTEL exams scheduled to be conducted on 24/25 April stand postponed until further notice. We will keep you informed of the potential dates for the exams as the situation improves and we finalize the same. Thanks and Regards, NPTEL TEAM.

Exam Format - April 25,2021

Dear Candidate, ****This is applicable only for the exam registered candidates**** Type of exam will be available in the list: Click Here You will have to appear at the allotted exam center and produce your Hall ticket and Government Photo Identification Card (Example: Driving License, Passport, PAN card, Voter ID, Aadhaar-ID with your Name, date of birth, photograph and signature) for verification and take the exam in person.  You can find the final allotted exam center details in the hall ticket. The hall ticket is yet to be released . We will notify the same through email and SMS. Type of exam: Computer based exam (Please check in the above list corresponding to your course name) The questions will be on the computer and the answers will have to be entered on the computer; type of questions may include multiple choice questions, fill in the blanks, essay-type answers, etc. Type of exam: Paper and pen Exam  (Please check in the above list corresponding to your course name) The questions will be on the computer. You will have to write your answers on sheets of paper and submit the answer sheets. Papers will be sent to the faculty for evaluation. On-Screen Calculator Demo Link: Kindly use the below link to get an idea of how the On-screen calculator will work during the exam. https://tcsion.com/ OnlineAssessment/ ScientificCalculator/ Calculator.html NOTE: Physical calculators are not allowed inside the exam hall. -NPTEL Team

Introduction to Machine Learning : Week 12 Feedback Form

Introduction to machine learning : week 12 is live now.

Dear students The lecture videos for Week-12 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=122&lesson=123 Practice Assignment for Week-12 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=122&assessment=143   Assignment for Week-12 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=122&assessment=169 The assignment has to be submitted on or before Wednesday, [14-04-2021, 23:59 IST] .   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 11 Feedback Form

Introduction to machine learning : week 11 is live now.

Dear students The lecture videos for Week-11 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=117&lesson=118 Practice Assignment for Week-11 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=117&assessment=144   Assignment for Week-11 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=117&assessment=167 The assignment has to be submitted on or before Wednesday, [07-04-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Assignment 9 Re-evaluation !!

Dear Learners, Re-evaluation has been done by making the weightage as 0 for Question 6 in Assignment 9. Students are requested to find their revised scores of Assignment 9 on the Progress page. Thanks & Regards, NPTEL Team

Introduction to Machine Learning : Week 10 Feedback Form

Introduction to machine learning : assignment 9 reevaluation.

Dear Learner, Assignment 9 submission of all students have been reevaluated by changing the answer for question number 6. Students are requested to find their revised scores of Assignment 9 in the Progress page. Thanks & Regards, -NPTEL Team.

Introduction to Machine Learning : Week 10 is live now!!

Dear students The lecture videos for Week-10 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=110&lesson=111 Practice Assignment for Week-10 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=110&assessment=142   Assignment for Week-10 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=110&assessment=165 The assignment has to be submitted on or before Wednesday, [31-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 9 Feedback Form

Introduction to machine learning : assignment 7 reevaluation.

Dear Learner Assignment 7 submission of all students has been reevaluated after the ignoring question number 2. Students are requested to find their revised scores of Assignment 7 in the Progress page. Thanks & Regards, - NPTEL Team.

Introduction to Machine Learning : Week 9 is live now!!

Dear students The lecture videos for Week-9 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=103&lesson=104 Practice Assignment for Week-9 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=103&assessment=141   Assignment for Week-9 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=103&assessment=163 The assignment has to be submitted on or before Wednesday, [24-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Dear Learner, Assignment 7 submission of all students has been reevaluated after the ignoring question number 4. Students are requested to find their revised scores of Assignment 7 in the Progress page. Thanks & Regards, -NPTEL Team.

Introduction to Machine Learning : Week 8 Feedback Form

Introduction to machine learning : week 8 is live now.

Dear students The lecture videos for Week-8 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=96&lesson=97 Practice Assignment for Week-8 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=96&assessment=140   Assignment for Week-8 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=96&assessment=162 The assignment has to be submitted on or before Wednesday, [17-03-2021, 23:59 IST] .   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 7 Feedback Form

Introduction to machine learning : week 7 is live now.

Dear students The lecture videos for Week-7 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=87&lesson=88 Practice Assignment for Week-7 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=87&assessment=139   Assignment for Week-7 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=87&assessment=159 The assignment has to be submitted on or before Wednesday, [10-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 6 Feedback Form

Introduction to machine learning : week 6 is live now.

Dear students The lecture videos for Week-6 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=76&lesson=77 Practice Assignment for Week-6 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=76&assessment=138   Assignment for Week-6 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=76&assessment=156 The assignment has to be submitted on or before Wednesday, [03-03-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 5 Feedback Form

Introduction to machine learning : assignment 3 reevaluation.

Dear Learner, Assignment 3 submission of all students have been reevaluated by changing the answer for question number 8 . Students are requested to find their revised scores of Assignment 3 in the Progress page. Thanks & Regards, -NPTEL Team.

Introduction to Machine Learning : Week 5 is live now!!

Dear students The lecture videos for Week-5 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=65&lesson=66 Practice Assignment for Week-5 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=65&assessment=137   Assignment for Week-5 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=65&assessment=154 The assignment has to be submitted on or before Wednesday, [24-02-2021, 23:59 IST] .   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Week 4 Feedback Form

Introduction to machine learning : assignment 1 reevaluation .

Dear Learner, Assignment 1 submission of all students have been reevaluated by adding the answer for question number 1 . Students are requested to find their revised scores of Assignment 1 in the Progress page. Thanks & Regards, -NPTEL Team.

Introduction to Machine Learning : Week 4 is live now!!

Dear students The lecture videos for Week-4 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=55&lesson=56 Practice Assignment for Week-4 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=55&assessment=136   Assignment for Week-4 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=55&assessment=152 The assignment has to be submitted on or before Wednesday, [17-02-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Feedback on Text Transcripts (English) of NPTEL videos

Dear Learners, We have uploaded the English transcripts for this course already. We would like to hear from you, a quick feedback for the same. Please take a minute to fill out this form. Click here  to fill the form -NPTEL Team

Introduction to Machine Learning : Week 3 Feedback Form

Introduction to machine learning : week 3 is live now.

Dear students The lecture videos for Week-3 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=45&lesson=46 Practice Assignment for Week-3 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=45&assessment=135   Assignment for Week-3 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=45&assessment=148 The assignment has to be submitted on or before Wednesday, [10-02-2021, 23:59 IST] .   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Introduction to Machine Learning : Assignment 2 due date has been extended!!

Dear Learners, Assignment 2  has been released already and the due date for the assignment has been extended Due date of assignment 2 is  Sunday, 07-02-2021, 23:59 IST Please note that there will not be any extension for the upcoming assignments. Note:  Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately.   Thanks & Regards, -NPTEL Team

Week 2 Feedback Form : Introduction to Machine Learning

Dear Learners, Thank you for continuing with the course and hope you are enjoying it. We would like to know if the expectations with which you joined this course are being met and hence please do take 2 minutes to fill out our weekly feedback form. It would help us tremendously in gauging the learner experience. Here is the link to the form:  https://docs.google.com/forms/d/1dnM4PbDMOxdQO7mUQSpKWNbFNn1OJ9ZLp-Szases-O8/viewform Thanks & Regards -NPTEL team

[NOC21-CS24] Clarification in Q8 of assignment 2

Dear Learner, In the 8th question of assignment-2, the representation vector for the word "Waffle" should be [6,4,0].  The given rules to find the feature vector are correct. In case of any doubt, feel free to ask on the forum. Regards, TAs

Introduction to Machine Learning : Week 2 is live now!!

Dear students The lecture videos for Week-2 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=34&lesson=35 Practice Assignment for Week-2 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=34&assessment=134   Assignment for Week-2 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=34&assessment=147 The assignment has to be submitted on or before Wednesday, [03-02-2021, 23:59 IST].   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

Week 1 Feedback Form : Introduction to Machine Learning

Introduction to machine learning : week 1 is live now.

Dear students The lecture videos for Week-1 have been uploaded for the course  Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=23&lesson=24 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment for Week-1 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=23&assessment=133   Assignment for Week-1 is also uploaded and can be accessed from the following link:  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=23&assessment=145 The assignment has to be submitted on or before Wednesday, [03-02-2021, 23:59 IST] .   As we have done so far, please use the discussion forums if you have any questions on this module. Note: Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. -NPTEL Team

NPTEL: Exam Registration is open now for Jan 2021 courses!

Dear Learner,  Here is the much-awaited announcement on registering for the Jan 2021 NPTEL course certification exam.  1. The registration for the certification exam is open only to those learners who have enrolled in the course.  2. If you want to register for the exam for this course, login here using the same email id which you had used to enroll to the course in Swayam portal. Please note that Assignments submitted through the exam registered email id ALONE will be taken into consideration towards final consolidated score & certification.  3 .  Date of exam: April 25, 2021 Certification exam registration URL is:  https://examform.nptel.ac. in/   Choose from the Cities where exam will be conducted:  Exam Cities   4. Exam fees:  If you register for the exam and pay before  Mar 8, 2021, 10:00 AM,  Exam fees will be  Rs. 1000/- per exam .  If you register for exam before  Mar 8, 2021 , 10:00 AM  and have not paid or if you register between  Mar 8, 2021, 10:00 AM & Mar 12, 2021, 5:00 PM,  Exam fees will be  Rs. 1500/-  per exam  5. 50% fee waiver for the following categories:  Students belonging to the SC/ST category: please select Yes for the SC/ST option and upload the correct Community certificate. Students belonging to the PwD category with more than 40% disability: please select Yes for the option and upload the relevant Disability certificate.  6. Last date for exam registration: Mar 12, 2021 5:00 PM (Friday).  7. Mode of payment: Online payment - debit card/credit card/net banking.  8. HALL TICKET:  The hall ticket will be available for download tentatively by  2 weeks prior to the exam date .  We will confirm the same through an announcement once it is published.  9. FOR CANDIDATES WHO WOULD LIKE TO WRITE MORE THAN 1 COURSE EXAM:- you can add or delete courses and pay separately – till the date when the exam form closes. Same day of exam – you can write exams for 2 courses in the 2 sessions. Same exam center will be allocated for both the sessions.  10.  Data changes:  Last date for data changes: Mar 12, 2021, 5:00 PM:  All the fields in the Exam form except for the following ones can be changed until the form closes.  The following 6 fields can be changed ONLY when there are NO courses in the course cart. And you will be able to edit the following fields only if you: -  REMOVE unpaid courses from the cart And/or - CANCEL paid courses  1. Do you come under the SC/ST category? *  2. SC/ST Proof  3. Are you a person with disabilities? *  4. Are you a person with disabilities above 40%?  5. Disabilities Proof  6. What is your role ?  Note:  Once you remove or cancel a course, you will be able to edit these fields immediately.  But, for cancelled courses, refund of fees will be initiated only after 2 weeks.  11.  LAST DATE FOR CANCELLING EXAMS and getting a refund: Mar 12, 2021, 5:00 PM  12. Click here to view Timeline and Guideline :  Guideline    Thanks & Regards, NPTEL TEAM

Introduction to Machine Learning : Week 0 is live now!!

Dear Learners,  We welcome you all to this course Introduction to Machine Learning . The assignment 0 has been released.  This assignment is based on prerequisite of the course.  You can find the assignment in the link :  https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=16&assessment=132 Due date of assignment 0 is  25-01-2021, 23:59 IST. Please note that this assignment is for practice and it will not be graded . Thanks & Regards  -NPTEL Team

Introduction to Machine Learning : Week 1 videos are live now!!

Dear Learners, The lecture videos for Week-1 have been uploaded for the course Introduction to Machine Learning . The lectures can be accessed using the following link: https://onlinecourses.nptel.ac.in/noc21_cs24/unit?unit=23&lesson=24 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already).   As we have done so far, please use the discussion forums if you have any questions on this module. - NPTEL Team

Welcome to NPTEL Online Course - Jan 2021!!

• Every week, about 2.5 to 4 hours of videos containing content by the Course instructor will be released along with an assignment based on this. Please watch the lectures, follow the course regularly and submit all assessments and assignments before the due date. Your regular participation is vital for learning and doing well in the course. This will be done week on week through the duration of the course.
• Please do the assignments yourself and even if you take help, kindly try to learn from it. These assignments will help you prepare for the final exams. Plagiarism and violating the Honor code will be taken very seriously if detected during the submission of assignments.
• The announcement group - will only have messages from course instructors and teaching assistants - regarding the lessons, assignments, exam registration, hall tickets etc.
• The discussion forum (Ask a question tab on the portal) - is for everyone to ask questions and interact.Anyone who knows the answers can reply to anyone's post and the course instructor/TA will also respond to your queries.
• Please make maximum use of this feature as this will help you learn much better.
• If you have any questions regarding the exam, registration, hall tickets, results, queries related to the technical content in the lectures, any doubts in the assignments, etc can be posted in the forum section
• The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
• The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
• Date and Time of Exams: April 25,2021 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
• Registration url: Announcements will be made when the registration form is open for registrations.
• The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
• Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.
• Once again, thanks for your interest in our online courses and certification. Happy learning. 

NPTEL : Keep in touch with us via Social Media

Dear Learner You already must know NPTEL is providing course certificates to those who complete the course successfully, with the learning happening right at your home or where you are. But NPTEL also keeps bringing out new initiatives and courses - which we would like to keep you posted on. Click the below links to like and follow us on Social Media for instant Updates: Facebook: https://www.facebook.com/NPTELNoc Twitter: https://twitter.com/nptelindia Linkedin: https://www.linkedin.com/in/nptel-india-085866ba/ Instagram: https://www.instagram.com/swayam_nptel/  Like and Follow us on Social Media. Let's create a better future by learning and growing together.  -NPTEL Team.

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An empirical assessment of different word embedding and deep learning models for bug assignment

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Bibliometrics & citations, view options, recommendations, a bug assignment approach combining expertise and recency of both bug fixing and source commits.

Automatic bug reports assignment to fixers is an important activity for software quality assurance. Existing approaches consider either the bug fixing or source commit activities which may result in inactive or inexperienced developers suggestions. ...

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects. Several studies propose automating bug assignment ...

An Empirical Study on Factors Impacting Bug Fixing Time

Fixing bugs is an important activity of the software development process. A typical process of bug fixing consists of the following steps: 1) a user files a bug report, 2) the bug is assigned to a developer, 3) the developer fixes the bug, 4) changed ...

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